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|
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dataset_type = 'DeepFashionDataset' |
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data_root = 'data/DeepFashion/In-shop/' |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True), |
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dict(type='Resize', img_scale=(750, 1101), keep_ratio=True), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(750, 1101), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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data = dict( |
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imgs_per_gpu=2, |
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workers_per_gpu=1, |
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train=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', |
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img_prefix=data_root + 'Img/', |
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pipeline=train_pipeline, |
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data_root=data_root), |
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val=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', |
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img_prefix=data_root + 'Img/', |
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pipeline=test_pipeline, |
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data_root=data_root), |
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test=dict( |
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type=dataset_type, |
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ann_file=data_root + |
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'annotations/DeepFashion_segmentation_gallery.json', |
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img_prefix=data_root + 'Img/', |
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pipeline=test_pipeline, |
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data_root=data_root)) |
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evaluation = dict(interval=5, metric=['bbox', 'segm']) |
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|
